Cross-Domain Few-Shot Remote Sensing Object Classification via Triplet Relation-Aware Metric

In real-world scenarios, peculiar remote sensing categories are difficult to collect on account of high cost and technical requirements. Moreover, there exists domain distribution gap among different datasets. Existing methods leverage inter-class and intra-class relations to enhance feature representation. Since remote images are shot from top to bottom, there is little difference between classes. Thus, such distance constraint only forms decision boundary between different classes. This paper proposes a triplet relation-aware metric for cross-domain few-shot remote sensing object classification, where the triplet relation-aware metric adjusts the distances among three kinds of inter-instance relations (i.e., same instance, same class and different class relations) to obtain a precise and effective feature representation. Especially, the distance of the same instance is regarded as a distance coordinate origin to guide distance metric learning. In this way, we constitute richer feature relations to promote representation learning in the source domain. Concretely, this procedure is optimized by the supervision of the designed relation-aware soft label based on the distance coordinate origin. Then, we align the triplet relation-aware metric between source domain and pseudo domain generated by the proposed episode style adversarial attack, thereby obtaining a domain-invariant feature representation. Extensive experiments on five widely-used remote sensing datasets demonstrate the superior performance of the proposed method compared with the state of the arts. Code is available at: https://github.com/jackhdpbl/TRAM.